Background: CAD/CAM has considerably transformed the clinical practice of dentistry. In particular, advanced dental materials produced via digital technologies offer unquestionable benefits, such as ideal mechanical stability, outstanding aesthetics and reliable high precision. Additive manufacturing (AM) technology has promoted new innovations, especially in the field of biomedicine. Aims: The aim of this study is to analyze the fracture resistance of implant-supported 3D-printed temporary crowns relative to milled crowns by compression testing. Methods: The study sample included 32 specimens of temporary crowns, which were divided into 16 specimens per group. Each group consisted of eight maxillary central incisor crowns (tooth 11) and eight maxillary molar crowns (tooth 16). The first group (16 specimens) was 3D printed by a mask printer (Varseo, BEGO, Bremen, Germany) with a temporary material (VarseoSmile Temp A3, BEGO, Bremen, Germany). The second group was milled with a millable temporary material (VitaCAD Temp mono-color, Vita, Bad Säckingen, Germany). The two groups were compression tested until failure to estimate their fracture resistance. The loading forces and travel distance until failure were measured. The statistical analysis was performed using SPSS Version 24.0. We performed multiple t tests and considered a significance level of p <0.05. Results: The mean fracture force of the printed molars was 1189.50 N (±250.85) with a deformation of 1.75 mm (±0.25). The milled molars reached a mean fracture force of 1817.50 N (±258.22) with a deformation of 1.750 mm (±0.20). The printed incisors fractured at 321.63 N (±145.90) with a deformation of 1.94 mm (±0.40), while the milled incisors fractured at 443.38 N (±113.63) with a deformation of 2.26 mm (±0.40). The milled molar group revealed significantly higher mechanical fracture strength than the 3D-printed molar group (P<0.001). However, no significant differences between the 3D-printed incisors and the milled incisors were found (p = 0.084). There was no significant difference in the travel distance until fracture for both the molar group (p = 1.000) and the incisor group (p = 0.129). Conclusion: Within the limits of this in vitro investigation, printed and milled temporary crowns withstood masticatory forces and were safe for clinical use.

Osteoporosis, a skeletal disorder, is expected to affect 60% of women aged over 50 years. Dual-energy X-ray absorptiometry (DXA) scans, the current gold standard, are typically used post-fracture, highlighting the need for early detection tools. Panoramic radiographs (PRs), common in annual dental evaluations, have been explored for osteoporosis detection using deep learning, but methodological flaws have cast doubt on otherwise optimistic results. This study aims to develop a robust artificial intelligence (AI) application for accurate osteoporosis identification in PRs, contributing to early and reliable diagnostics. A total of 250 PRs from three groups (A: osteoporosis group, B: non-osteoporosis group matching A in age and gender, C: non-osteoporosis group differing from A in age and gender) were cropped to the mental foramen region. A pretrained convolutional neural network (CNN) classifier was used for training, testing, and validation with a random split of the dataset into subsets (A vs. B, A vs. C). Detection accuracy and area under the curve (AUC) were calculated. The method achieved an F1 score of 0.74 and an AUC of 0.8401 (A vs. B). For young patients (A vs. C), it performed with 98% accuracy and an AUC of 0.9812. This study presents a proof-of-concept algorithm, demonstrating the potential of deep learning to identify osteoporosis in dental radiographs. It also highlights the importance of methodological rigor, as not all optimistic results are credible.

Objectives: Dental implant placement requires exceptional precision to ensure functional and esthetic success. Traditional guidance methods, such as static drilling guides and dynamic navigation systems, have improved accuracy but are limited by high costs, rigidity, and reliance on specialized hardware. This study introduces an augmented reality (AR) system using consumer smartphones for real‐time navigation in dental implant placement. The system aims to provide a cost‐effective, eco‐friendly alternative to conventional methods by integrating virtual planning with physical models.Material and Methods: A modified dental training model with removable parallel pins served as the physical component. Implant positions were digitally planned and color‐coded using 3D scanning and modeling software, then integrated into an AR application built with Unity Engine. A smartphone's camera was calibrated to project virtual overlays onto the physical model. In vitro testing evaluated alignment accuracy, drill guidance, and system performance under controlled lighting conditions.Results: The AR system successfully aligned virtual overlays with the physical model, providing effective visual guidance for implant drill positioning. Operators maintained planned trajectories, demonstrating the feasibility of AR as an alternative to static and dynamic guidance systems. Challenges included the system's sensitivity to stable lighting and visual cues.Conclusions: This AR‐based approach offers an accessible and sustainable solution for modern dental implantology. Future research will focus on quantitative accuracy assessments, AI integration for enhanced performance, and clinical trials to validate real‐world applicability. AR technology has the potential to transform dental practices by improving outcomes while reducing costs and environmental impact.

Background: Osteoporosis, a systemic skeletal disorder, is expected to affect 60% of women over 50. While dual-energy X-ray absorptiometry (DXA) scans are the current gold standard for diagnosis, they are typically used only after fractures occur, highlighting the need for early detection tools. Initial studies have shown panoramic radiographs (PRs) to be a potential medium, but these have methodological flaws. This study aims to address these shortcomings by developing a robust AI application for accurate osteoporosis identification in PRs. Methods: A total of 348 PRs were used for development, 58 PRs for validation, and 51 PRs for hold-out testing. Initially, the YOLOv8 object detection model was employed to predict the regions of interest. Subsequently, the predicted regions of interest were extracted from the PRs and processed by the EfficientNet classification model. Results: The model for osteoporosis detection on a PR achieved an overall sensitivity of 0.83 and an F1-score of 0.53. The area under the curve (AUC) was 0.76. The lowest detection sensitivity was for the cropped angulus region (0.66), while the highest sensitivity was for the cropped mental foramen region (0.80). Conclusion: This research presents a proof-of-concept algorithm showing the potential of deep learning to identify osteoporosis in dental radiographs. Furthermore, our thorough evaluation of existing algorithms revealed that many optimistic outcomes lack credibility when subjected to rigorous methodological scrutiny.